A pseudo principal component analysis method for multi-dimensional open-high-low-close data in candlestick chart

Abstract

As the most widely-used data form in the field of finance, the open-high-low-close (OHLC) data is being collected by all kinds of financial trading systems all the time. This paper puts forward a pseudo-principal component analysis (PCA) for multi-dimensional OHLC data, which can extract their useful information in a comprehensible way for visualization and easy interpretation. Firstly, a novel feature-based representation for OHLC data is proposed, which contains fruitful and explicit economic implications. Next, we define a full set of numerical characteristics and variance-covariance structures for the feature-based OHLC data. Then, the pseudo-PCA procedure for OHLC data is deduced based on the proposed algebraic operators. Finally, the effectiveness and interpretability of the proposed pseudo-PCA method are verified through finite simulations and three typical empirical experiments. This paper enriches the application scenarios of classical PCA and contributes to the multivariate statistical modeling of symbolic data. The proposed applications can serve as models for related studies.

Keywords:

• OHLC data
• pseudo-PCA
• dimension reduction
• feature information extraction

Conflicts of interest

Author Wenyang Huang declares that he has no conflict of interest. Author Huiwen Wang declares that she has no conflict of interest. Author Shanshan Wang declares that she has no conflict of interest.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Data availability statement

The data used in this paper is downloaded form Wind and Refinitiv. The data can be uploaded as required.

Code availability

This paper applies custom R code and can be uploaded as required.

Additional information

Funding

This study was supported by a grant from the National Natural Science Foundation of China (grant numbers. 72021001, 71420107025, 11701023).